Data augmentation by a CycleGAN-based extra-supervised model for nondestructive testing

Author:

Jiangsha AiORCID,Tian Lulu,Bai Libing,Zhang Jie

Abstract

Abstract The deep learning method is widely used in computer vision tasks with large-scale annotated datasets. However, obtaining such datasets in most directions of the vision based nondestructive testing (NDT) field is very challenging. Data augmentation is proved as an efficient way of dealing with the lack of large-scale annotated datasets. In this paper, we propose a CycleGAN-based extra-supervised (CycleGAN-ES) model to generate synthetic NDT images, where the ES is used to ensure that the bidirectional mapping is learned for corresponding labels and defects. Furthermore, we show the effectiveness of using the synthesized images to train deep convolutional neural networks (DCNNs) for defect recognition. In the experiments, we extract a number of x-ray welding images with both defect and no defects from the published GDXray dataset, and CycleGAN-ES is used to generate the synthetic defect images based on a small number of extracted defect images and manually drawn labels that are used as a content guide. For quality verification of the synthesized defect images, we use a high-performance classifier pretrained using a big dataset to recognize the synthetic defects and show the comparability of the performances of classifiers trained using synthetic defects and real defects, respectively. To present the effectiveness of using the synthesized defects as an augmentation method, we train and evaluate the performances of DCNN for defect recognition with or without the synthesized defects.

Publisher

IOP Publishing

Subject

Applied Mathematics,Instrumentation,Engineering (miscellaneous)

Reference48 articles.

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3